import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest

import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed


lowerCamelCase__  =					logging.getLogger(__name__)


def 				lowerCAmelCase__       (			a__=2    ,     a__=3    ,     a__=16    ,     a__ = 10    ,     a__ = 2 )      ->Optional[int]:




  '''simple docstring'''

  def get_dataset(a__ ):
    _UpperCamelCase           =    torch.randn(batch_size * n_batches    ,     1 )
    return TensorDataset(a__    ,     a * x + b + 0.1 * torch.randn(batch_size * n_batches    ,     1 ) )

  _UpperCamelCase           =    get_dataset(a__ )
  _UpperCamelCase           =    get_dataset(a__ )
  _UpperCamelCase           =    DataLoader(a__    ,     shuffle=a__    ,     batch_size=a__    ,     num_workers=4 )
  _UpperCamelCase           =    DataLoader(a__    ,     shuffle=a__    ,     batch_size=a__    ,     num_workers=4 )
  return (train_dataloader, valid_dataloader)


def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__    ,     a__    ,     a__    ,     a__=None )      ->Dict:




  '''simple docstring'''

  _UpperCamelCase           =    []
  for epoch in range(a__ ):
    # Train quickly
    model.train()
    for batch in dataloader:
      _UpperCamelCase      ,						_UpperCamelCase           =    batch
      _UpperCamelCase           =    model(a__ )
      _UpperCamelCase           =    torch.nn.functional.mse_loss(a__    ,     a__ )
      accelerator.backward(a__ )
      optimizer.step()
      optimizer.zero_grad()
    rands.append(random.random() )  # Introduce some randomness
    if scheduler is not None:
      scheduler.step()
  return rands




class 					_UpperCAmelCase			(     nn.Module  ):



 '''simple docstring'''




 def __init__(		self				:   Dict)						->	int:


   """simple docstring"""







   super().__init__()
   _UpperCamelCase           =    nn.Parameter(torch.randn(1))
   _UpperCamelCase           =    nn.Parameter(torch.randn(1))







 def 		__UpperCAmelCase				(		self				:   List[str]							, lowercase_				:   int)						->	Dict:


   """simple docstring"""







   return x * self.a + self.b




class 					_UpperCAmelCase			(     unittest.TestCase  ):



 '''simple docstring'''




 def 		__UpperCAmelCase				(		self				:   Any)						->	Optional[int]:


   """simple docstring"""







   with tempfile.TemporaryDirectory() as tmpdir:
     set_seed(42)
     _UpperCamelCase           =    DummyModel()
     _UpperCamelCase           =    torch.optim.Adam(params=model.parameters()							, lr=1e-3)
     _UpperCamelCase      ,						_UpperCamelCase           =    dummy_dataloaders()
     _UpperCamelCase           =    ProjectConfiguration(total_limit=1							, project_dir=lowercase_							, automatic_checkpoint_naming=lowercase_)
     # Train baseline
     _UpperCamelCase           =    Accelerator(project_config=lowercase_)
     _UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase           =    accelerator.prepare(
         lowercase_							, lowercase_							, lowercase_							, lowercase_)
     # Save initial
     accelerator.save_state()

     # Save second state
     accelerator.save_state()
     self.assertEqual(len(os.listdir(accelerator.project_dir))							, 1)







 def 		__UpperCAmelCase				(		self				:   int)						->	Tuple:


   """simple docstring"""







   with tempfile.TemporaryDirectory() as tmpdir:
     set_seed(42)
     _UpperCamelCase           =    DummyModel()
     _UpperCamelCase           =    torch.optim.Adam(params=model.parameters()							, lr=1e-3)
     _UpperCamelCase      ,						_UpperCamelCase           =    dummy_dataloaders()
     # Train baseline
     _UpperCamelCase           =    Accelerator()
     _UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase           =    accelerator.prepare(
         lowercase_							, lowercase_							, lowercase_							, lowercase_)
     # Save initial
     _UpperCamelCase           =    os.path.join(lowercase_							, "initial")
     accelerator.save_state(lowercase_)
     ((_UpperCamelCase)      ,						(_UpperCamelCase))           =    model.a.item(), model.b.item()
     _UpperCamelCase           =    optimizer.state_dict()
     _UpperCamelCase           =    train(3							, lowercase_							, lowercase_							, lowercase_							, lowercase_)
     ((_UpperCamelCase)      ,						(_UpperCamelCase))           =    model.a.item(), model.b.item()
     _UpperCamelCase           =    optimizer.state_dict()

     # Train partially
     set_seed(42)
     _UpperCamelCase           =    DummyModel()
     _UpperCamelCase           =    torch.optim.Adam(params=model.parameters()							, lr=1e-3)
     _UpperCamelCase      ,						_UpperCamelCase           =    dummy_dataloaders()
     _UpperCamelCase           =    Accelerator()
     _UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase           =    accelerator.prepare(
         lowercase_							, lowercase_							, lowercase_							, lowercase_)
     accelerator.load_state(lowercase_)
     ((_UpperCamelCase)      ,						(_UpperCamelCase))           =    model.a.item(), model.b.item()
     _UpperCamelCase           =    optimizer.state_dict()
     self.assertEqual(lowercase_							, lowercase_)
     self.assertEqual(lowercase_							, lowercase_)
     self.assertEqual(lowercase_							, lowercase_)

     _UpperCamelCase           =    train(2							, lowercase_							, lowercase_							, lowercase_							, lowercase_)
     # Save everything
     _UpperCamelCase           =    os.path.join(lowercase_							, "checkpoint")
     accelerator.save_state(lowercase_)

     # Load everything back in and make sure all states work
     accelerator.load_state(lowercase_)
     test_rands += train(1							, lowercase_							, lowercase_							, lowercase_							, lowercase_)
     ((_UpperCamelCase)      ,						(_UpperCamelCase))           =    model.a.item(), model.b.item()
     _UpperCamelCase           =    optimizer.state_dict()
     self.assertEqual(lowercase_							, lowercase_)
     self.assertEqual(lowercase_							, lowercase_)
     self.assertEqual(lowercase_							, lowercase_)
     self.assertEqual(lowercase_							, lowercase_)







 def 		__UpperCAmelCase				(		self				:   List[str])						->	Any:


   """simple docstring"""







   with tempfile.TemporaryDirectory() as tmpdir:
     set_seed(42)
     _UpperCamelCase           =    DummyModel()
     _UpperCamelCase           =    torch.optim.Adam(params=model.parameters()							, lr=1e-3)
     _UpperCamelCase      ,						_UpperCamelCase           =    dummy_dataloaders()
     _UpperCamelCase           =    ProjectConfiguration(automatic_checkpoint_naming=lowercase_)

     # Train baseline
     _UpperCamelCase           =    Accelerator(project_dir=lowercase_							, project_config=lowercase_)
     _UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase           =    accelerator.prepare(
         lowercase_							, lowercase_							, lowercase_							, lowercase_)
     # Save initial
     accelerator.save_state()
     ((_UpperCamelCase)      ,						(_UpperCamelCase))           =    model.a.item(), model.b.item()
     _UpperCamelCase           =    optimizer.state_dict()
     _UpperCamelCase           =    train(3							, lowercase_							, lowercase_							, lowercase_							, lowercase_)
     ((_UpperCamelCase)      ,						(_UpperCamelCase))           =    model.a.item(), model.b.item()
     _UpperCamelCase           =    optimizer.state_dict()

     # Train partially
     set_seed(42)
     _UpperCamelCase           =    DummyModel()
     _UpperCamelCase           =    torch.optim.Adam(params=model.parameters()							, lr=1e-3)
     _UpperCamelCase      ,						_UpperCamelCase           =    dummy_dataloaders()
     _UpperCamelCase           =    ProjectConfiguration(iteration=1							, automatic_checkpoint_naming=lowercase_)
     _UpperCamelCase           =    Accelerator(project_dir=lowercase_							, project_config=lowercase_)
     _UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase           =    accelerator.prepare(
         lowercase_							, lowercase_							, lowercase_							, lowercase_)
     accelerator.load_state(os.path.join(lowercase_							, "checkpoints"							, "checkpoint_0"))
     ((_UpperCamelCase)      ,						(_UpperCamelCase))           =    model.a.item(), model.b.item()
     _UpperCamelCase           =    optimizer.state_dict()
     self.assertEqual(lowercase_							, lowercase_)
     self.assertEqual(lowercase_							, lowercase_)
     self.assertEqual(lowercase_							, lowercase_)

     _UpperCamelCase           =    train(2							, lowercase_							, lowercase_							, lowercase_							, lowercase_)
     # Save everything
     accelerator.save_state()

     # Load everything back in and make sure all states work
     accelerator.load_state(os.path.join(lowercase_							, "checkpoints"							, "checkpoint_1"))
     test_rands += train(1							, lowercase_							, lowercase_							, lowercase_							, lowercase_)
     ((_UpperCamelCase)      ,						(_UpperCamelCase))           =    model.a.item(), model.b.item()
     _UpperCamelCase           =    optimizer.state_dict()
     self.assertEqual(lowercase_							, lowercase_)
     self.assertEqual(lowercase_							, lowercase_)
     self.assertEqual(lowercase_							, lowercase_)
     self.assertEqual(lowercase_							, lowercase_)







 def 		__UpperCAmelCase				(		self				:   str)						->	int:


   """simple docstring"""







   _UpperCamelCase           =    torch.tensor([1, 2, 3])
   _UpperCamelCase           =    torch.tensor([2, 3, 4])
   _UpperCamelCase           =    DummyModel()
   _UpperCamelCase           =    torch.optim.Adam(net.parameters())
   _UpperCamelCase           =    Accelerator()
   with self.assertRaises(lowercase_) as ve:
     accelerator.register_for_checkpointing(lowercase_							, lowercase_							, lowercase_							, lowercase_)
   _UpperCamelCase           =    str(ve.exception)
   self.assertTrue("Item at index 0" in message)
   self.assertTrue("Item at index 1" in message)
   self.assertFalse("Item at index 2" in message)
   self.assertFalse("Item at index 3" in message)







 def 		__UpperCAmelCase				(		self				:   int)						->	Optional[Any]:


   """simple docstring"""







   with tempfile.TemporaryDirectory() as tmpdir:
     set_seed(42)
     _UpperCamelCase           =    DummyModel()
     _UpperCamelCase           =    torch.optim.Adam(params=model.parameters()							, lr=1e-3)
     _UpperCamelCase           =    torch.optim.lr_scheduler.StepLR(lowercase_							, step_size=1							, gamma=0.99)
     _UpperCamelCase      ,						_UpperCamelCase           =    dummy_dataloaders()
     _UpperCamelCase           =    ProjectConfiguration(automatic_checkpoint_naming=lowercase_)
     # Train baseline
     _UpperCamelCase           =    Accelerator(project_dir=lowercase_							, project_config=lowercase_)
     _UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase           =    accelerator.prepare(
         lowercase_							, lowercase_							, lowercase_							, lowercase_							, lowercase_)
     # Save initial
     accelerator.save_state()
     _UpperCamelCase           =    scheduler.state_dict()
     train(3							, lowercase_							, lowercase_							, lowercase_							, lowercase_							, lowercase_)
     self.assertNotEqual(lowercase_							, scheduler.state_dict())

     # Load everything back in and make sure all states work
     accelerator.load_state(os.path.join(lowercase_							, "checkpoints"							, "checkpoint_0"))
     self.assertEqual(lowercase_							, scheduler.state_dict())







 def 		__UpperCAmelCase				(		self				:   int)						->	str:


   """simple docstring"""







   with tempfile.TemporaryDirectory() as tmpdir:
     set_seed(42)
     _UpperCamelCase           =    DummyModel()
     _UpperCamelCase           =    ProjectConfiguration(automatic_checkpoint_naming=lowercase_							, total_limit=2)
     # Train baseline
     _UpperCamelCase           =    Accelerator(project_dir=lowercase_							, project_config=lowercase_)
     _UpperCamelCase           =    accelerator.prepare(lowercase_)
     # Save 3 states:
     for _ in range(11):
       accelerator.save_state()
     self.assertTrue(not os.path.exists(os.path.join(lowercase_							, "checkpoints"							, "checkpoint_0")))
     self.assertTrue(os.path.exists(os.path.join(lowercase_							, "checkpoints"							, "checkpoint_9")))
     self.assertTrue(os.path.exists(os.path.join(lowercase_							, "checkpoints"							, "checkpoint_10")))







 @require_cuda
 def 		__UpperCAmelCase				(		self				:   Union[str, Any])						->	Optional[int]:


   """simple docstring"""







   _UpperCamelCase           =    ["torchrun", f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__)]
   execute_subprocess_async(lowercase_							, env=os.environ.copy())


if __name__ == "__main__":
   lowerCamelCase__  =					'''/tmp/accelerate/state_checkpointing'''
   lowerCamelCase__  =					DummyModel()
   lowerCamelCase__  =					torch.optim.Adam(params=model.parameters(), lr=1e-3)
   lowerCamelCase__  =					torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
   lowerCamelCase__,lowerCamelCase__  =					dummy_dataloaders()
   lowerCamelCase__  =					ProjectConfiguration(automatic_checkpoint_naming=True)
   # Train baseline
   lowerCamelCase__  =					Accelerator(project_dir=savedir, project_config=project_config, mixed_precision='''no''')
   if accelerator.process_index == 0:
      if os.path.exists(savedir):
         shutil.rmtree(savedir)
      os.makedirs(savedir)
   lowerCamelCase__,lowerCamelCase__,lowerCamelCase__,lowerCamelCase__,lowerCamelCase__  =					accelerator.prepare(
       model, optimizer, train_dataloader, valid_dataloader, scheduler
   )
   lowerCamelCase__,lowerCamelCase__  =					accelerator.prepare(model, optimizer)
   train(3, model, train_dataloader, optimizer, accelerator, scheduler)
   # Check that the intial optimizer is loaded on the GPU
   for group in optimizer.param_groups:
      lowerCamelCase__  =					group['''params'''][0].device
      break
   assert param_device.type == accelerator.device.type
   lowerCamelCase__  =					model.cpu()
   accelerator.wait_for_everyone()
   accelerator.save_state()
   accelerator.wait_for_everyone()

   # Check CPU state
   accelerator.load_state(os.path.join(savedir, '''checkpoints''', '''checkpoint_0'''), map_location='''cpu''')
   for group in optimizer.param_groups:
      lowerCamelCase__  =					group['''params'''][0].device
      break
   assert (
       param_device.type == torch.device('''cpu''').type
   ), F"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"

   # Check device state
   model.to(accelerator.device)
   accelerator.load_state(os.path.join(savedir, '''checkpoints''', '''checkpoint_0'''), map_location='''on_device''')
   for group in optimizer.param_groups:
      lowerCamelCase__  =					group['''params'''][0].device
      break
   assert (
       param_device.type == accelerator.device.type
   ), F"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"

   # Check error
   with pytest.raises(TypeError, match='''Unsupported optimizer map location passed'''):
      accelerator.load_state(os.path.join(savedir, '''checkpoints''', '''checkpoint_0'''), map_location='''invalid''')
   accelerator.wait_for_everyone()
   if accelerator.process_index == 0:
      shutil.rmtree(savedir)
   accelerator.wait_for_everyone()





from typing import Optional
from urllib.parse import quote

import huggingface_hub as hfh
from packaging import version


def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__ = None )      ->str:




  '''simple docstring'''

  if version.parse(hfh.__version__ ).release < version.parse("0.11.0" ).release:
    # old versions of hfh don't url-encode the file path
    _UpperCamelCase           =    quote(a__ )
  return hfh.hf_hub_url(a__    ,     a__    ,     repo_type="dataset"    ,     revision=a__ )




